Prediction of carbon monoxide acute air pollution episodes. Model formulation and first application in Lombardy

This paper describes a method to forecast the exceedance probability of a fixed threshold for a certain air pollutant concentration. This general approach has been applied in the case of carbon monoxide on 35 traffic monitoring stations in the Lombardy Region. The implemented model has been called FOREPOLL (Forecast Pollution). The model structure, consisting in three basic modules (the deterministic, the stochastic and the Bayesian one), has been thought to be adjustable to different stations of an air quality network and to various pollutants. Forepoll uses the last biennium data set of the station in exam (pollutants and micrometeorological parameters) as a moving learning time and also needs the forecasted synoptic weather type. In the first module the daily maxima of the hourly measured pollutant concentrations are checked in order to eliminate some known physical dependencies, such as the temperature dependence of the emissions. Then the data are divided into subgroups depending on the weather type and in each group fitted by a different Weibull distribution. To provide a first a priori exceedance probability, this distribution is daily rebuilt by taking into account the forecasted parameters. In the last module this probability is enhanced or reduced using simple, experience based, bayesian rules providing the a posteriori exceedance probability. Model validation trials have been carried out on a year of CO forecasted concentrations in different air quality stations; the first results are quite good particularly for the metropolitan areas, because the model seems to work better in case of stronger and more diffuse pollution.